Overview

Dataset statistics

Number of variables15
Number of observations940
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory110.3 KiB
Average record size in memory120.1 B

Variable types

NUM14
CAT1

Warnings

TotalDistance is highly correlated with TotalSteps and 1 other fieldsHigh correlation
TotalSteps is highly correlated with TotalDistance and 1 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 1 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with ModeratelyActiveDistanceHigh correlation
ModeratelyActiveDistance is highly correlated with FairlyActiveMinutesHigh correlation
ActivityDate is uniformly distributed Uniform
TotalSteps has 77 (8.2%) zeros Zeros
TotalDistance has 78 (8.3%) zeros Zeros
TrackerDistance has 78 (8.3%) zeros Zeros
LoggedActivitiesDistance has 908 (96.6%) zeros Zeros
VeryActiveDistance has 413 (43.9%) zeros Zeros
ModeratelyActiveDistance has 386 (41.1%) zeros Zeros
LightActiveDistance has 85 (9.0%) zeros Zeros
SedentaryActiveDistance has 858 (91.3%) zeros Zeros
VeryActiveMinutes has 409 (43.5%) zeros Zeros
FairlyActiveMinutes has 384 (40.9%) zeros Zeros
LightlyActiveMinutes has 84 (8.9%) zeros Zeros

Reproduction

Analysis started2022-06-30 11:18:13.694073
Analysis finished2022-06-30 11:18:37.865206
Duration24.17 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Id
Real number (ℝ≥0)

Distinct33
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4855407369
Minimum1503960366
Maximum8877689391
Zeros0
Zeros (%)0.0%
Memory size7.3 KiB
2022-06-30T07:18:37.908277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1503960366
5-th percentile1624580081
Q12320127002
median4445114986
Q36962181067
95-th percentile8792009665
Maximum8877689391
Range7373729025
Interquartile range (IQR)4642054065

Descriptive statistics

Standard deviation2424805476
Coefficient of variation (CV)0.4994030966
Kurtosis-1.273030694
Mean4855407369
Median Absolute Deviation (MAD)2418762951
Skewness0.1771248993
Sum4.564082927e+12
Variance5.879681595e+18
MonotocityIncreasing
2022-06-30T07:18:38.014470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
1503960366313.3%
 
4319703577313.3%
 
8583815059313.3%
 
8378563200313.3%
 
8053475328313.3%
 
7086361926313.3%
 
6962181067313.3%
 
5553957443313.3%
 
4702921684313.3%
 
4558609924313.3%
 
Other values (23)63067.0%
 
ValueCountFrequency (%) 
1503960366313.3%
 
1624580081313.3%
 
1644430081303.2%
 
1844505072313.3%
 
1927972279313.3%
 
ValueCountFrequency (%) 
8877689391313.3%
 
8792009665293.1%
 
8583815059313.3%
 
8378563200313.3%
 
8253242879192.0%
 

ActivityDate
Categorical

UNIFORM

Distinct31
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
4/12/2016
 
33
4/14/2016
 
33
4/15/2016
 
33
4/13/2016
 
33
4/23/2016
 
32
Other values (26)
776 
ValueCountFrequency (%) 
4/12/2016333.5%
 
4/14/2016333.5%
 
4/15/2016333.5%
 
4/13/2016333.5%
 
4/23/2016323.4%
 
4/29/2016323.4%
 
4/28/2016323.4%
 
4/26/2016323.4%
 
4/25/2016323.4%
 
4/24/2016323.4%
 
Other values (21)61665.5%
 
2022-06-30T07:18:38.131256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-06-30T07:18:38.333457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.725531915
Min length8

TotalSteps
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct842
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7637.910638
Minimum0
Maximum36019
Zeros77
Zeros (%)8.2%
Memory size7.3 KiB
2022-06-30T07:18:38.436931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13789.75
median7405.5
Q310727
95-th percentile15485.1
Maximum36019
Range36019
Interquartile range (IQR)6937.25

Descriptive statistics

Standard deviation5087.150742
Coefficient of variation (CV)0.6660395732
Kurtosis1.169111169
Mean7637.910638
Median Absolute Deviation (MAD)3446.5
Skewness0.6528949353
Sum7179636
Variance25879102.67
MonotocityNot monotonic
2022-06-30T07:18:38.594818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0778.2%
 
24420.2%
 
670820.2%
 
916720.2%
 
617520.2%
 
1053820.2%
 
151020.2%
 
853820.2%
 
793720.2%
 
436320.2%
 
Other values (832)84589.9%
 
ValueCountFrequency (%) 
0778.2%
 
410.1%
 
810.1%
 
910.1%
 
1610.1%
 
ValueCountFrequency (%) 
3601910.1%
 
2932610.1%
 
2774510.1%
 
2362910.1%
 
2318610.1%
 

TotalDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct615
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.489702122
Minimum0
Maximum28.03000069
Zeros78
Zeros (%)8.3%
Memory size7.3 KiB
2022-06-30T07:18:38.714856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.619999886
median5.244999886
Q37.712499976
95-th percentile11.65649963
Maximum28.03000069
Range28.03000069
Interquartile range (IQR)5.09250009

Descriptive statistics

Standard deviation3.924605909
Coefficient of variation (CV)0.7149032537
Kurtosis3.113018353
Mean5.489702122
Median Absolute Deviation (MAD)2.560000062
Skewness1.126273627
Sum5160.319995
Variance15.40253154
MonotocityNot monotonic
2022-06-30T07:18:38.813600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0788.3%
 
2.59999990550.5%
 
0.0150.5%
 
3.91000008640.4%
 
4.94999980940.4%
 
1.78999996240.4%
 
4.32999992440.4%
 
2.68000006740.4%
 
3.5099999940.4%
 
4.90000009540.4%
 
Other values (605)82487.7%
 
ValueCountFrequency (%) 
0788.3%
 
0.0150.5%
 
0.0210.1%
 
0.02999999920.2%
 
0.03999999910.1%
 
ValueCountFrequency (%) 
28.0300006910.1%
 
26.7199993110.1%
 
25.2900009210.1%
 
20.6499996210.1%
 
20.3999996210.1%
 

TrackerDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct613
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.475351058
Minimum0
Maximum28.03000069
Zeros78
Zeros (%)8.3%
Memory size7.3 KiB
2022-06-30T07:18:38.920967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.619999886
median5.244999886
Q37.710000038
95-th percentile11.65649963
Maximum28.03000069
Range28.03000069
Interquartile range (IQR)5.090000152

Descriptive statistics

Standard deviation3.907275943
Coefficient of variation (CV)0.7136119496
Kurtosis3.20388914
Mean5.475351058
Median Absolute Deviation (MAD)2.555000305
Skewness1.134549615
Sum5146.829994
Variance15.2668053
MonotocityNot monotonic
2022-06-30T07:18:39.028114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0788.3%
 
2.59999990550.5%
 
0.0150.5%
 
3.91000008640.4%
 
2.68000006740.4%
 
1.78999996240.4%
 
4.32999992440.4%
 
4.94999980940.4%
 
3.5099999940.4%
 
8.73999977140.4%
 
Other values (603)82487.7%
 
ValueCountFrequency (%) 
0788.3%
 
0.0150.5%
 
0.0210.1%
 
0.02999999920.2%
 
0.03999999910.1%
 
ValueCountFrequency (%) 
28.0300006910.1%
 
26.7199993110.1%
 
25.2900009210.1%
 
20.6499996210.1%
 
20.3999996210.1%
 

LoggedActivitiesDistance
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1081709399
Minimum0
Maximum4.94214201
Zeros908
Zeros (%)96.6%
Memory size7.3 KiB
2022-06-30T07:18:39.110699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.94214201
Range4.94214201
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6198965182
Coefficient of variation (CV)5.730712138
Kurtosis41.29594078
Mean0.1081709399
Median Absolute Deviation (MAD)0
Skewness6.297440367
Sum101.6806835
Variance0.3842716933
MonotocityNot monotonic
2022-06-30T07:18:39.192077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
090896.6%
 
2.09214711291.0%
 
2.25308108370.7%
 
4.08169221910.1%
 
4.86179208810.1%
 
4.87823200210.1%
 
4.91236782110.1%
 
2.83232593510.1%
 
4.91114616410.1%
 
4.88560485810.1%
 
Other values (9)91.0%
 
ValueCountFrequency (%) 
090896.6%
 
1.95959603810.1%
 
2.09214711291.0%
 
2.25308108370.7%
 
2.78517508510.1%
 
ValueCountFrequency (%) 
4.9421420110.1%
 
4.93055009810.1%
 
4.92484092710.1%
 
4.91236782110.1%
 
4.91114616410.1%
 

VeryActiveDistance
Real number (ℝ≥0)

ZEROS

Distinct333
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.502680851
Minimum0
Maximum21.92000008
Zeros413
Zeros (%)43.9%
Memory size7.3 KiB
2022-06-30T07:18:39.298158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.209999993
Q32.05249995
95-th percentile6.403000092
Maximum21.92000008
Range21.92000008
Interquartile range (IQR)2.05249995

Descriptive statistics

Standard deviation2.658941165
Coefficient of variation (CV)1.769464995
Kurtosis11.9109508
Mean1.502680851
Median Absolute Deviation (MAD)0.209999993
Skewness2.996169988
Sum1412.52
Variance7.069968118
MonotocityNot monotonic
2022-06-30T07:18:39.409348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
041343.9%
 
0.0791.0%
 
0.05999999960.6%
 
0.14000000150.5%
 
0.33000001350.5%
 
0.34000000440.4%
 
1.05999994340.4%
 
0.36000001440.4%
 
1.0099999940.4%
 
2.78999996240.4%
 
Other values (323)48251.3%
 
ValueCountFrequency (%) 
041343.9%
 
0.0220.2%
 
0.03999999910.1%
 
0.05000000130.3%
 
0.05999999960.6%
 
ValueCountFrequency (%) 
21.9200000810.1%
 
21.6599998510.1%
 
13.3999996210.1%
 
13.2600002310.1%
 
13.2399997710.1%
 

ModeratelyActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct211
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5675425514
Minimum0
Maximum6.480000019
Zeros386
Zeros (%)41.1%
Memory size7.3 KiB
2022-06-30T07:18:39.510117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.239999995
Q30.800000012
95-th percentile2.130000114
Maximum6.480000019
Range6.480000019
Interquartile range (IQR)0.800000012

Descriptive statistics

Standard deviation0.8835803192
Coefficient of variation (CV)1.556852992
Kurtosis10.12562853
Mean0.5675425514
Median Absolute Deviation (MAD)0.239999995
Skewness2.771193607
Sum533.4899983
Variance0.7807141804
MonotocityNot monotonic
2022-06-30T07:18:39.619371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
038641.1%
 
0.20000000391.0%
 
0.28000000191.0%
 
0.40000000691.0%
 
0.2580.9%
 
0.31000000280.9%
 
0.93000000780.9%
 
0.41999998780.9%
 
0.27000001170.7%
 
0.56999999370.7%
 
Other values (201)48151.2%
 
ValueCountFrequency (%) 
038641.1%
 
0.0110.1%
 
0.0210.1%
 
0.02999999930.3%
 
0.03999999930.3%
 
ValueCountFrequency (%) 
6.48000001910.1%
 
6.21000003810.1%
 
5.59999990510.1%
 
5.40000009510.1%
 
5.23999977110.1%
 

LightActiveDistance
Real number (ℝ≥0)

ZEROS

Distinct491
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.340819149
Minimum0
Maximum10.71000004
Zeros85
Zeros (%)9.0%
Memory size7.3 KiB
2022-06-30T07:18:39.738267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.945000023
median3.364999891
Q34.782500148
95-th percentile6.462000036
Maximum10.71000004
Range10.71000004
Interquartile range (IQR)2.837500125

Descriptive statistics

Standard deviation2.040655388
Coefficient of variation (CV)0.6108248598
Kurtosis-0.1803002748
Mean3.340819149
Median Absolute Deviation (MAD)1.420000195
Skewness0.1822474682
Sum3140.37
Variance4.164274413
MonotocityNot monotonic
2022-06-30T07:18:39.846744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0859.0%
 
4.17999982860.6%
 
3.17000007660.6%
 
4.88000011460.6%
 
3.23000001960.6%
 
3.94000005750.5%
 
3.2599999950.5%
 
0.0150.5%
 
4.46000003850.5%
 
5.40999984750.5%
 
Other values (481)80685.7%
 
ValueCountFrequency (%) 
0859.0%
 
0.0150.5%
 
0.0210.1%
 
0.02999999930.3%
 
0.03999999910.1%
 
ValueCountFrequency (%) 
10.7100000410.1%
 
10.5699996910.1%
 
10.3000001910.1%
 
9.47999954210.1%
 
9.46000003810.1%
 

SedentaryActiveDistance
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001606382977
Minimum0
Maximum0.109999999
Zeros858
Zeros (%)91.3%
Memory size7.3 KiB
2022-06-30T07:18:39.939731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.01
Maximum0.109999999
Range0.109999999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.007346176309
Coefficient of variation (CV)4.573116384
Kurtosis99.12744363
Mean0.001606382977
Median Absolute Deviation (MAD)0
Skewness8.589899015
Sum1.509999998
Variance5.396630636e-05
MonotocityNot monotonic
2022-06-30T07:18:40.121681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
085891.3%
 
0.01505.3%
 
0.02212.2%
 
0.02999999940.4%
 
0.05000000130.3%
 
0.0710.1%
 
0.03999999910.1%
 
0.10999999910.1%
 
0.10000000110.1%
 
ValueCountFrequency (%) 
085891.3%
 
0.01505.3%
 
0.02212.2%
 
0.02999999940.4%
 
0.03999999910.1%
 
ValueCountFrequency (%) 
0.10999999910.1%
 
0.10000000110.1%
 
0.0710.1%
 
0.05000000130.3%
 
0.03999999910.1%
 

VeryActiveMinutes
Real number (ℝ≥0)

ZEROS

Distinct122
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.16489362
Minimum0
Maximum210
Zeros409
Zeros (%)43.5%
Memory size7.3 KiB
2022-06-30T07:18:40.226823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q332
95-th percentile93.05
Maximum210
Range210
Interquartile range (IQR)32

Descriptive statistics

Standard deviation32.84480306
Coefficient of variation (CV)1.551852972
Kurtosis5.778070106
Mean21.16489362
Median Absolute Deviation (MAD)4
Skewness2.176143214
Sum19895
Variance1078.781088
MonotocityNot monotonic
2022-06-30T07:18:40.326406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
040943.5%
 
1232.4%
 
2181.9%
 
3161.7%
 
8151.6%
 
6141.5%
 
11141.5%
 
19131.4%
 
5131.4%
 
14121.3%
 
Other values (112)39341.8%
 
ValueCountFrequency (%) 
040943.5%
 
1232.4%
 
2181.9%
 
3161.7%
 
4101.1%
 
ValueCountFrequency (%) 
21010.1%
 
20710.1%
 
20010.1%
 
19410.1%
 
18610.1%
 

FairlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct81
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.56489362
Minimum0
Maximum143
Zeros384
Zeros (%)40.9%
Memory size7.3 KiB
2022-06-30T07:18:40.426889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q319
95-th percentile51
Maximum143
Range143
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.98740395
Coefficient of variation (CV)1.473465588
Kurtosis7.99573138
Mean13.56489362
Median Absolute Deviation (MAD)6
Skewness2.47949196
Sum12751
Variance399.4963168
MonotocityNot monotonic
2022-06-30T07:18:40.570844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
038440.9%
 
8363.8%
 
6232.4%
 
5232.4%
 
16222.3%
 
7202.1%
 
10192.0%
 
9192.0%
 
13181.9%
 
11181.9%
 
Other values (71)35838.1%
 
ValueCountFrequency (%) 
038440.9%
 
1101.1%
 
280.9%
 
391.0%
 
4141.5%
 
ValueCountFrequency (%) 
14310.1%
 
12510.1%
 
12210.1%
 
11610.1%
 
11510.1%
 

LightlyActiveMinutes
Real number (ℝ≥0)

ZEROS

Distinct335
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.812766
Minimum0
Maximum518
Zeros84
Zeros (%)8.9%
Memory size7.3 KiB
2022-06-30T07:18:40.682214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1127
median199
Q3264
95-th percentile369.05
Maximum518
Range518
Interquartile range (IQR)137

Descriptive statistics

Standard deviation109.1746998
Coefficient of variation (CV)0.5662213247
Kurtosis-0.360117932
Mean192.812766
Median Absolute Deviation (MAD)69
Skewness-0.03792934281
Sum181244
Variance11919.11507
MonotocityNot monotonic
2022-06-30T07:18:40.793292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0848.9%
 
206121.3%
 
258101.1%
 
19591.0%
 
21480.9%
 
13970.7%
 
23870.7%
 
14170.7%
 
19970.7%
 
22770.7%
 
Other values (325)78283.2%
 
ValueCountFrequency (%) 
0848.9%
 
130.3%
 
240.4%
 
330.3%
 
410.1%
 
ValueCountFrequency (%) 
51810.1%
 
51310.1%
 
51210.1%
 
48710.1%
 
48010.1%
 

SedentaryMinutes
Real number (ℝ≥0)

Distinct549
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean991.2106383
Minimum0
Maximum1440
Zeros1
Zeros (%)0.1%
Memory size7.3 KiB
2022-06-30T07:18:40.886989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile536.7
Q1729.75
median1057.5
Q31229.5
95-th percentile1440
Maximum1440
Range1440
Interquartile range (IQR)499.75

Descriptive statistics

Standard deviation301.2674368
Coefficient of variation (CV)0.3039388654
Kurtosis-0.6659500269
Mean991.2106383
Median Absolute Deviation (MAD)261
Skewness-0.2944980897
Sum931738
Variance90762.06847
MonotocityNot monotonic
2022-06-30T07:18:41.006087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1440798.4%
 
118270.7%
 
69260.6%
 
111250.5%
 
113150.5%
 
112250.5%
 
110550.5%
 
70950.5%
 
111950.5%
 
72850.5%
 
Other values (539)81386.5%
 
ValueCountFrequency (%) 
010.1%
 
210.1%
 
1310.1%
 
4810.1%
 
11110.1%
 
ValueCountFrequency (%) 
1440798.4%
 
143930.3%
 
143830.3%
 
143720.2%
 
143110.1%
 

Calories
Real number (ℝ≥0)

Distinct734
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2303.609574
Minimum0
Maximum4900
Zeros4
Zeros (%)0.4%
Memory size7.3 KiB
2022-06-30T07:18:41.121020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1372.85
Q11828.5
median2134
Q32793.25
95-th percentile3654.25
Maximum4900
Range4900
Interquartile range (IQR)964.75

Descriptive statistics

Standard deviation718.1668621
Coefficient of variation (CV)0.3117571962
Kurtosis0.6250269361
Mean2303.609574
Median Absolute Deviation (MAD)467
Skewness0.4224504814
Sum2165393
Variance515763.6419
MonotocityNot monotonic
2022-06-30T07:18:41.271600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1980131.4%
 
2063111.2%
 
184191.0%
 
168891.0%
 
134780.9%
 
222540.4%
 
181940.4%
 
204440.4%
 
192240.4%
 
040.4%
 
Other values (724)87092.6%
 
ValueCountFrequency (%) 
040.4%
 
5210.1%
 
5710.1%
 
12010.1%
 
25710.1%
 
ValueCountFrequency (%) 
490010.1%
 
455210.1%
 
454710.1%
 
454610.1%
 
450110.1%
 

Interactions

2022-06-30T07:18:17.403413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:17.571512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:17.671579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:17.771654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:17.870909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:17.968909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.066804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.165804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.265802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.356763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.440336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.529990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.615921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.712636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.810721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:18.914819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.019719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.120717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.221719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.326705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.427703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.526817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.626215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.725757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.805758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:19.979505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.078498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.181605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.282608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.380776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.480680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.575415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.670417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.755481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.832481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.914679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:20.995679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.078423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.158422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.243149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.326219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.425056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.518306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.615278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.714318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.795525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.875391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:21.955393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.036526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.118526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.200487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.370858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.453913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.541051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.624893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.720822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.810867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.904390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:22.994817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.088623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.177692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.260136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.343581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.435746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.517556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.602556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.683559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.769401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.878875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:23.975952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:24.066318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:24.194808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:24.344193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:24.575337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:24.679512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:24.769179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:24.883252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.074805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.170503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.259293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.350116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.435117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.524355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.615392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.711325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.808652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:25.901656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.003844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.089844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.177139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.264794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.365322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.465201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.568072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.657178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.751617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.851617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:26.956743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.051355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.143580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.244217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.325573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.411124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.595216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.677723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.762423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.845826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:27.933863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.020436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.109030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.195029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.285236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.372227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.466625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.568937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.675015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.760238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.859855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:28.969873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.092468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.224320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.326410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.415684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.504755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.600952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.695408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.814679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.909231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:29.995886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:30.219408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:30.318797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:30.421303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:30.539955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:30.652995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:30.757452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:30.859482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:30.954679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.068808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.159821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.278820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.387117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.484395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.606524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.715168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.823783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:31.919438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:32.028437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:32.142157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:32.242436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:32.353943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:32.446280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:32.559172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:32.662366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:32.777375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.008241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.112336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.211421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.301959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.389717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.473781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.570340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.673845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.767408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.874633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:33.971689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.071397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.182674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.281902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.370490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.487780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.599960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.702025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.815600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:34.924670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.036478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.134744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.242887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.335329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.450457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.654677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.764441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.862118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:35.967954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.084314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.178398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.269773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.373034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.497968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.599065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.703136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.805258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:36.913611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:37.013747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:37.115935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:37.224177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:37.332322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-06-30T07:18:41.380950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-30T07:18:41.570540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-30T07:18:41.754252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-30T07:18:42.053912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-30T07:18:37.520867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-30T07:18:37.752580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
015039603664/12/2016131628.508.500.01.880.556.060.025133287281985
115039603664/13/2016107356.976.970.01.570.694.710.021192177761797
215039603664/14/2016104606.746.740.02.440.403.910.0301118112181776
315039603664/15/201697626.286.280.02.141.262.830.029342097261745
415039603664/16/2016126698.168.160.02.710.415.040.036102217731863
515039603664/17/201697056.486.480.03.190.782.510.038201645391728
615039603664/18/2016130198.598.590.03.250.644.710.0421623311491921
715039603664/19/2016155069.889.880.03.531.325.030.050312647752035
815039603664/20/2016105446.686.680.01.960.484.240.028122058181786
915039603664/21/201698196.346.340.01.340.354.650.01982118381775

Last rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
93088776893915/3/2016108188.2100008.2100000.01.390.106.670.0119322911892817
93188776893915/4/20161819316.29999916.2999990.010.420.315.530.0066821211543477
93288776893915/5/20161405510.67000010.6700000.05.460.824.370.00671518811703052
93388776893915/6/20162172719.34000019.3400000.012.790.296.160.00961723210954015
93488776893915/7/2016123328.1300008.1300000.00.080.966.990.001052827110364142
93588776893915/8/2016106868.1100008.1100000.01.080.206.800.0017424511742847
93688776893915/9/20162022618.25000018.2500000.011.100.806.240.05731921711313710
93788776893915/10/2016107338.1500008.1500000.01.350.466.280.00181122411872832
93888776893915/11/20162142019.55999919.5599990.013.220.415.890.00881221311273832
93988776893915/12/201680646.1200006.1200000.01.820.044.250.002311377701849